Most recent works focus on answering first order logical queries to explore the knowledge graph reasoning via multi-hop logic predictions. However, existing reasoning models are limited by the circumscribed logical paradigms of training samples, which leads to a weak generalization of unseen logic. To address these issues, we propose a plug-in module called Logic Diffusion (LoD) to discover unseen queries from surroundings and achieves dynamical equilibrium between different kinds of patterns. The basic idea of LoD is relation diffusion and sampling sub-logic by random walking as well as a special training mechanism called gradient adaption. Besides, LoD is accompanied by a novel loss function to further achieve the robust logical diffusion when facing noisy data in training or testing sets. Extensive experiments on four public datasets demonstrate the superiority of mainstream knowledge graph reasoning models with LoD over state-of-the-art. Moreover, our ablation study proves the general effectiveness of LoD on the noise-rich knowledge graph.

翻译：大多数近期工作聚焦于回答一阶逻辑查询，通过多跳逻辑预测探索知识图谱推理。然而，现有推理模型受限于训练样本的有限逻辑范式，导致对未见逻辑的泛化能力较弱。针对这些问题，我们提出一种名为逻辑扩散（Logic Diffusion, LoD）的插件模块，用于从环境中发现未见查询，并实现不同模式间的动态均衡。LoD的核心思想是通过随机游走进行关系扩散与子逻辑采样，并采用一种名为梯度自适应的特殊训练机制。此外，LoD伴随一种新颖的损失函数，以在面对训练或测试集中的噪声数据时进一步增强鲁棒逻辑扩散能力。在四个公开数据集上的大量实验表明，集成LoD的主流知识图谱推理模型相较于现有最优方法具有优越性。同时，我们的消融研究证明LoD在富含噪声的知识图谱上具有普遍有效性。